This presentation introduces the Genetic Algorithms + Feature Importance Feature Selection technique, implemented in the open source Python package felimination. Genetic algorithms are a powerful optimization technique that can be effectively utilized for feature selection in machine learning models. By combining genetic algorithms with feature importance, we aim to enhance the feature selection process, leading to more robust and interpretable models. We will start by reviewing genetic algorithms, detailing the steps of pool initialization, crossover, mutation, and selection. The presentation will continue by showcasing some code snippets using felimination, a Python package containing a suite of algorithms for feature selection, including the genetic algorithm with feature importance selector. Claudio Salvatore Arcidiacono is a Senior Machine Learning Engineer at Mollie. He has been working in the fintech sector over the past 7 years with lots of experience in classical machine learning problems. He loves to contribute to data science open source libraries like feature engine, scikit-learn, and narwhals. He maintains a couple of open source libraries himself (felimination and sklearo). In his free time, he is a coffee scientist, using a data-driven approach to dial in the perfect cup of espresso.